Evolving optimal agendas for package deal negotiation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Fatima:2011:GECCO,

• author = "Shaheen Fatima and Ahmed Kattan",
• title = "Evolving optimal agendas for package deal negotiation",
• booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation",
• year = "2011",
• editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
• isbn13 = "978-1-4503-0557-0",
• pages = "505--512",
• keywords = "genetic algorithms, genetic programming, Evolutionary combinatorial optimization and metaheuristics",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Dublin, Ireland",
• DOI = "doi:10.1145/2001576.2001646",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper presents a hyper GA system to evolve optimal agendas for package deal negotiation. The proposed system uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to speed up the evolution. The negotiation scenario is as follows. There are two negotiators/agents (a and b) and m issues/items available for negotiation. But from these m issues, the agents must choose g issues and negotiate on them. The g issues thus chosen form the agenda. The agenda is important because the outcome of negotiation depends on it. Furthermore, a and b will, in general, get different utilities/profits from different agendas. Thus, for competitive negotiation (i.e., negotiation where each agent wants to maximise its own utility), each agent wants to choose an agenda that maximizes its own profit. However, the problem of determining an agent's optimal agenda is complex, as it requires combinatorial search. To overcome this problem, we present a hyper GA method that uses a Surrogate Model based on Radial Basis Function Networks (RBFNs) to find an agent's optimal agenda. The performance of the proposed method is evaluated experimentally. The results of these experiments demonstrate that the surrogate assisted algorithm, on average, performs better than standard GA and random search.",
• notes = "Also known as \cite{2001646} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)",

}

Genetic Programming entries for Shaheen Fatima Ahmed Kattan

Citations